Many disciplines can benefit from pattern recognition. Disciplines where the benefit is greatest share characteristics and needs. Some common characteristics include large volumes of data, anomalous zones of interest that are mixed together with a large number of similar non-anomalous zones, timeframes too short to allow rigorous manual examination, and anomalies that manifest themselves in many ways, no two of which are exactly the same. Analysis of the data is usually done by highly trained professionals working on tight time schedules. Examples of these disciplines include, but are not limited to, hydrocarbon exploration and medical testing.
Exploring for hydrocarbon reservoirs is a very competitive process. Decisions affecting large amounts of capital investment are made in a time-constrained environment based on massive amounts of technical data. The process begins with physical measurements that indicate the configuration and selected properties of subsurface strata in an area of interest. A variety of mathematical manipulations of the data are performed by computer to form displays that are used by an interpreter, who interprets the data in view of facts and theories about the subsurface. The interpretations may lead to decisions for bidding on leases or drilling of wells.
A commonly used measurement for studying the subsurface of the earth under large geographical areas is seismic signals (acoustic waves) that are introduced into the subsurface and reflected back to measurement stations on or near the surface of the earth. Processing of seismic data has progressed hand-in-hand with the increased availability and capabilities of computer hardware. Calculations performed per mile of seismic data collected have increased many-fold in the past few years. Display hardware for observation by a human interpreter has become much more versatile.
When an interpreter makes decisions from the seismic, and other, data it is used with some knowledge of geology of the area being investigated. The decisions involve identification, analysis, and evaluation of the geological components of an oilfield, which include the presence of a reservoir rock, presence of hydrocarbons, and the presence of a container or trap. The rationale for the decisions that were made was based on both the geologic information and the data. That rationale is not generally documented in detail for seismic data analysis due to the large amount of data and information being analyzed. Therefore, it is difficult to review the history of exploration decisions and repeat the decision process using conventional procedures. The relative importance attached to the many characteristics shown in the seismic data and known from the geology is a subjective value that does not become a part of the record of the exploration process.
It is recognized that seismic data can also be used to obtain detailed information regarding producing oil or gas reservoirs and to monitor changes in the reservoir caused by fluid movement. Description of neural network modeling for seismic pattern recognition or seismic facies analysis in an oil reservoir is described, for example, in “Seismic-Pattern Recognition Applied to an Ultra Deep-Water Oilfield,” Journal of Petroleum Technology August, 2001, page 41). Time-lapse seismic measurements for monitoring fluid movement in a reservoir are well known. The fluid displacement may be caused by natural influx of reservoir fluid, such as displacement of oil by water or gas, or may be caused by injection of water, steam or other fluids. Pressure depletion of a reservoir may also cause changes in seismic wave propagation that can be detected. From these data, decisions on where to drill wells, production rates of different wells and other operational decisions may be made. The neural network technique usually assumes that all significant combinations of rock type are known before analysis is started so that they can be used as a training set. This assumption is usually acceptable when analyzing fully developed fields but breaks down when only a few or no wells have been drilled. Common implementations of the neural network technique usually assume selection of the location of the geology of interest is an input that is determined prior to the analysis and often selects it using an analysis gate of fixed thickness. As the geology of interest is not always well known, the geology of interest should be a product of the analysis, not an input. Moreover, geology of interest rarely has a fixed thickness. The thickness varies significantly as the depositional process varies from place to place, sometimes by an amount that is sufficient to significantly degrade the result of the neural network analysis. This form of analysis includes information extraction and information classification in a single step that has little of no user control.
U.S. Pat. No. 6,336,943 B1 discloses a neural network-based system for delineating spatially dependent objects in the subsurface from seismic data. The application of neural networks to seismic data interpretation has been widely investigated.
U.S. Pat. No. 6,336,596 B1 discloses the use of a Voxel Coupling Matrix, which is developed using a finite number of neighboring voxels forming a textile. “Texture attributes” are developed. The attribute volumes are then used as inputs into an attribute-trace classification method to produce a seismic interpretation volume. The interpretation volume is displayed using distinct colors to represent various numbers of classes of reflection patterns present within the seismic volume. The aforementioned technique is an example of a class of image analysis called texture matching. These techniques have a significant trade off. While larger textiles (blocks of neighboring voxels) give better results, larger textiles smear out and blur the resulting image. Smaller textiles give better image resolution and edges but give poorer results. Success in finding a textile size that gives results of adequate quality with sufficiently small blurring is difficult often very difficult, especially when the rock layers are dipping rather than horizontally flat. The analysis classifies textures which is only one class of attributes the others being features and patterns.
U.S. Pat. No. 6,151,555 discloses a workstation computer system and an associated method and program storage device. U.S. Pat. No. 6,131,071 discloses a method for processing seismic data to provide improved quantification and visualization of subtle seismic thin bed tuning effects and other lateral rock discontinuities. A reflection from a thin bed has a characteristic expression and the frequency domain that is indicative of the thickness of the bed. The method may be applied to any collection of spatially related seismic traces. Other methods of presentation of seismic data are disclosed in the patent and technical literature.
What is needed is a way to perform unsupervised pattern analysis that does not require a learning set, does not require texture matching, does not classify attributes of a single spatial size, and does not require a-priori knowledge of the location of the geology of interest. Unsupervised pattern analysis requires feature, pattern, and texture extraction from seismic data where the features, patterns, and texture measurements are well chosen for optimal classification and can be interpreted in terms of oilfield components. Optimal means that they:                Do not require a learning set;        Is capable of finding matches to an example data set, if any;        Have variable spatial lengths of extracted attributes so that they track geology;        Have the minimum number of attributes to maximize computation simplicity;        Have an adequate number of attributes to separate out the rock types as uniquely as the seismic data allows;        Are interpretable and intuitive to geoscientists in that they measure the visual characteristics of the data that the geoscientists use when they visually classify the data;        Determine the locations of the different rock types as a product of the analysis;        Perform analysis of several spatial sizes of attributes; and        Perform classification based on several types of attributes including features, patterns, and textures in a structure recognizing the different levels of abstraction.        
There is further a need in the art to have a process of creating features, patterns and textures, from data plus a data hierarchy recognizing the relative levels of abstraction along with a pattern database containing all of the information.
From a production standpoint, there is a need in the art to visually classify this information to analyze the interior of a hydrocarbon reservoir more effectively. Direct hydrocarbon indicators should be visually identifiable. Seismic stratigraphy should be performed in a way that includes visual classification of all the seismic stratigraphic information available in the data. In addition the knowledge inherent in the visual classification needs to be captured in a template, stored in a template library, and reused later in an automatic process.
While 3D seismic produces images of structures and features of the subsurface of the earth over very large geographical areas, it does not interpret those images. A trained geoscientist or specialist performs the interpretation. Unfortunately, reliance upon a relatively few qualified individuals increases the cost of the interpretation process and limits the number of interpretations that can be made within a given period. This makes current seismic interpretation techniques impractical for the analysis of the very large volumes of seismic data that are currently available. As a result of the large and growing amount of available data, there is a need in the art for a knowledge capture technique where the information in the 3D seismic data that the specialist looks at is captured by a pattern recognition process. Ideally, the pattern recognition process would be repeated for large amounts of data in a screening process, with the results displayed in an intuitive manner so that the specialist can quickly perform quality control on the results, and correct noise induced errors, if any.
There is further a need in the art for a way to auto-track textures, patterns, and features in order to isolate and measure rock bodies or objects of interest. Preferably, an object should be auto-tracked so that its location is determined both by the properties of its interface with surrounding objects and by the difference between the features, patterns, and textures in the objects interior when compared to those outside the object. This tracks the object directly rather than tracking the object solely based on the varying properties of the interface which, by itself, is unlikely to be as descriptive of the object. Interface tracking tracks the object indirectly, as would be accomplished with boundary representations. An example of automatically detecting objects based on their interior and interface characteristics would be in colorectal cancer screening where the target anomaly (a colorectal polyp) has both distinctive interface and interior characteristics.
Moreover, a data analysis specialist should not be required to rely on analysis of non-visual measures of object characteristics. The information describing the visual characteristics of seismic data should be stored in a way that allows the data specialist to interact with the information to infer and extract geological information and to make a record of the exploration process. Finally, a way should be provided to analyze geologic information with varying levels of abstraction.
The above-identified needs are shared across many disciplines yet the specific nature and the characteristics of the anomalies vary across disciplines and sometimes within a single problem. Thus there is a need for a common method of analysis that is capable of being applied to a wide variety of data types and problems, yet it is capable of being adapted to the specific data and problem being solved in situations where required.